Detection of attention deficit hyperactivity disorder based on EEG signals using Least Square Support Vector Machine (LS-SVM)

Abstract

ABSTRACT Attention Deficit Hyperactivity Disorder (ADHD) is a mental disorder affecting children in their early stages. Detection of ADHD is considered a challenging task for experts because there is no accurate test to determine whether a child has ADHD or not. Mainly, most of previous studies are used electroencephalography (EEG) signals to detect ADHD. In this study, a robust model for ADHD detection-integrated discrete wavelet transform (DWT), statistical features, and a least square support vector machine (LS-SVM) is proposed to detect ADHD from EEG signals. The EEG signals are decomposed into five bands and then, a set of statistical features are tested to find the optimal feature sets using K-means model. A public dataset is used to evaluate the proposed model. A total of 45 ADHD patients and 45 healthy are involved to evaluate the proposed model. Several metrics are used to evaluate the proposed model including 10-fold cross-validation precision, sensitivity, and specificity metrics. A channel selection is also investigated, and we found that the channels ‘P3, P7, Pz’ are more important and gave better prediction results than other channels. The proposed model obtained an average accuracy of 98.06% with LS-SVM, and 95.46% with the KNN classifier.